CN112954739A - Millimeter wave MEC unloading transmission method based on circular game algorithm - Google Patents

Millimeter wave MEC unloading transmission method based on circular game algorithm Download PDF

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CN112954739A
CN112954739A CN202110102950.5A CN202110102950A CN112954739A CN 112954739 A CN112954739 A CN 112954739A CN 202110102950 A CN202110102950 A CN 202110102950A CN 112954739 A CN112954739 A CN 112954739A
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魏庆
石嘉
周奕帆
赵钟灵
胡俊凡
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Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
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Abstract

The invention discloses a millimeter wave MEC unloading transmission method based on a circular game algorithm, which mainly solves the problems that in the prior art, a millimeter wave MEC calculation unloading transmission strategy is only suitable for a single user scene, the unloading transmission energy efficiency is low, and the transmission delay is long. The implementation scheme is as follows: 1) setting user matching parameters during unloading transmission by using a non-cooperative game theory method; 2) calculating utility functions of all the matching pairs; 3) judging whether the matching of the matching pair is successful or not by utilizing a utility function; 4) repeated matching judgment is carried out on the matching pairs which are successfully matched, the matching pair with the maximum utility function is reserved, and the rest matching pairs are broken; 5) and (3) detecting the matching completion condition, if the matching is completed, performing non-orthogonal multiple access (NOMA) transmission on the obtained user sequence through matching, and completing MEC data unloading, otherwise, returning to the step 3). The invention greatly reduces the required energy efficiency and transmission time delay, and can be used for communication transmission based on millimeter waves.

Description

Millimeter wave MEC unloading transmission method based on circular game algorithm
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a millimeter wave MEC unloading method which can be used for millimeter wave communication transmission.
Background
In the future 5G/B5G communication, "high computation traffic" will show explosive growth, such as virtual reality applications, ultra-clear video streaming, large-scale man-machine interaction games, AI computation processing, and the like, and the mobile terminal faces unprecedented overload computation challenges, which easily causes serious problems such as service delay or interruption, instantaneous power consumption surge, and the like. The mobile edge computing MEC is a distributed computing technology, adopts a distributed cloud architecture, directly unloads computing tasks to nearby infrastructure, namely a micro base station provided with an MEC server, reduces needed computing delay and local energy consumption of users, and can greatly reduce the load of a single computing server, thereby better solving the problem of computing unloading of a mobile terminal. Therefore, in a 5G/B5G mobile network, MEC technology can adapt to a variety of different business scenarios, including smart mobile terminals, VR virtual reality applications, holographic video or imagery, unmanned internet of vehicles.
The data transmission scheme of the existing MEC technology includes two types, one type of computation offload transmission is mainly based on decimetric wave frequency band communication and may be referred to as a "decimetric wave mect technology", and the other type of computation offload transmission is mainly based on millimeter wave frequency band communication and may be referred to as a "millimeter wave MEC technology". With the miniaturization and the densification of the 5G/B5G communication network, the quantity of high-computation-capacity services is greatly increased, the frequency spectrum resources of the traditional decimetric wave communication are limited, and the large data volume is required to be unloaded and transmitted while carrying intensive computation tasks, so that the computation tasks are overtime and even fail to work. Therefore, how to optimize the energy transmission efficiency and reduce the transmission delay is an important issue in the MEC transmission problem.
The millimeter wave MEC has millimeter wave communication with rich spectrum resources, can naturally serve the MEC technology, greatly reduces the time delay of a calculation task by realizing high-speed MEC unloading transmission, and further supports large-scale calculation task unloading. Compared with the visible millimeter wave MEC technology, the method has great advantages for the characteristics and the performance of different calculation unloading technologies according to the existing research. However, most of the related documents in the prior art are limited to the millimeter wave MEC in the calculation decision problem, that is, the decision calculation task is executed at the user side or the edge server side. Some millimeter wave MEC calculation unloading transmission strategies are only suitable for single user scenes, and have low unloading transmission energy efficiency and large transmission delay.
Disclosure of Invention
The invention aims to provide a millimeter wave MEC unloading and transmitting method based on a circular game algorithm, so that unloading and transmitting energy efficiency and transmission delay are optimized to the greatest extent in a multi-user scene.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
1. a millimeter wave MEC unloading transmission method based on a circular game algorithm is characterized by comprising the following steps:
1) the non-cooperative game theory method is utilized to set user matching parameters during unloading transmission:
setting transmission power, transmission rate, matching probability and penalty coefficient;
setting a fixed user and a non-fixed user;
setting a pairing set NP, an unpaired set UP and a paired set AP of the current round;
defining a reward function as the product of the matching probability and the transmission rate;
defining a penalty function as a linear weighting of the transmission power and its inverse;
2) each fixed user and each non-fixed user form a matching pair, and the reward function R of each matching pair is calculatedi,j(S) and a penalty function Ci,j(S) and summing the two functions to obtain a utility function Ui,j(S), wherein i represents the ith fixed user, j represents the jth non-fixed user, and S represents a pairing set type;
3) according to the maximum utility function rule, fixed users m sequentially search optimal non-fixed users n for matching;
4) judging whether the current matching pair in 3) and the existing matching pair have the same non-fixed user n:
if so, perform 5);
if not, then execute 6);
5) comparing the utility function of the current matching pair in 3) with the utility function of the 'existing matching pair' of the non-fixed user n:
if in 3)Utility function U of front matching pairsm,n(S) is larger, the corresponding matching pair (m, n) is successfully matched, and the existing matching pair is broken;
otherwise, returning to 3), the fixed user m selects the non-fixed user n' with suboptimal performance;
6) checking whether all fixed users finish pairing:
if yes, completing user matching, and performing unloading transmission on each group of paired users based on the NOMA mechanism to complete MEC data unloading;
otherwise, return to 3).
The invention adopts the matching pair transmission process based on the circulating game, and has the following advantages:
firstly, compared with the existing traversal algorithm, the time complexity O (n ^2) approaching a quadratic polynomial can be reduced to the time complexity O (n) approaching a first-order polynomial.
Secondly, compared with the existing greedy algorithm, when the number of the matched pairs and the total transmission energy are respectively changed, the energy efficiency-time delay balance function can be better optimized.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
fig. 2 is a graph of energy efficiency versus delay function for simulated matching pairs when different numbers of pairs are considered using the present invention and a greedy algorithm of the prior art.
Fig. 3 is a graph of energy efficiency-delay tradeoff functions for a simulated matched pair when increasing total transmitted energy using the present invention and a prior greedy algorithm.
Detailed description of the invention
In order to make the object and technical solution of the present invention clearer and clearer, embodiments and effects of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, the implementation steps of this example are as follows:
and step 1, setting user matching parameters during unloading transmission by using a non-cooperative game theory method.
The non-cooperative game theory method belongs to game theory, is an important branch of modern mathematics operational research, and means that when one party determines game strategies in a non-cooperative game process with limited game times, if the strategies selected by the other party in the game are the best strategies based on the game strategy combination, the strategy aggregate solutions selected by the two parties can reach a stable optimal solution.
The step sets user matching parameters during unloading transmission according to a non-cooperative game theory method, and the method is realized as follows:
1.1) setting transmission power W, transmission rate r, probability matching probability p for selecting the pairing set of the current round, matching probability q for selecting the unpaired set of the current round, and two penalty coefficients k1 and k2 with different values;
1.2) the game-setting parties have 2N, wherein the fixed users have N, the non-fixed users have N, the fixed users respectively represent fixed users i with fixed transmission time and non-fixed users j with unfixed transmission time, i, j is in the middle of {1,2, … N }, and the transmission time T of the non-fixed users isjLess than or equal to the transmission time T of the fixed useri；
1.3) setting a pairing set NP, an unpaired set UP and a paired set AP of the current round;
1.4) defining a reward function Ri,j(S) is the match probability betai,j(S) product with transmission rate r; defining a penalty function Ci,j(S) is a linear weighting of the transmission power W and its inverse;
1.5) defining a utility function Ui,jAnd (S) is the sum of the reward function and the penalty function.
And 2, calculating a user utility function.
2.1) forming a matching pair by each fixed user i and each non-fixed user j, wherein i, j belongs to {1,2, … N }, and N represents the total number of the matching pairs;
2.2) calculating the matching probability of each matching pair:
Figure BDA0002916674380000041
wherein, p represents the probability of selecting the pairing set NP of the current round, q represents the probability of selecting the unpaired set UP of the current round, i represents the ith fixed user, j represents the jth non-fixed user, and S represents the pairing set type;
2.3) calculating the reward function of the matching pair according to the matching probability, namely multiplying the matching probability of the matching pair by the transmission rate R to obtain the reward function R of the matching pairi,j(S)：
Ri,j(S)＝βi,j(S)·r；
2.4) calculating the penalty function C of each matching pairi,j(S), that is, the transmission power is linearly weighted with its inverse:
Ci,j(S)＝k1·W+k2/W，
wherein k is1,k2Are two penalty factors of different values, taken in this example but not limited to k1＝0.7，k20.3W is transmission power;
2.5) reward function Ri,j(S) and a penalty function Ci,j(S) summing to obtain utility function U of each matching pairi,j(S)：
Figure BDA0002916674380000042
And 3, judging whether the matching pair is successfully matched or not by using the utility function.
3.1) judging whether the matching pair of the fixed user is successfully matched according to the maximum utility function rule:
selecting a maximum value of a utility function of a matching pair formed by each fixed user i and all non-fixed users j, wherein the fixed users are successfully paired, and the rest are the fixed users which are not successfully paired;
3.2) fixed users i sequentially search the optimal non-fixed users j for matching:
3.2.1) forming matching pairs by using an unpaired successful fixed user i and all non-fixed users j, and calculating utility function values U of the matching pairsi,j(S)，j∈{1,2,…N}；
3.2.2) from Ui,1(S)～Ui,N(S) selecting the maximum value U of the utility functioni,n(S), then the maximum value U of the functioni,n(S) corresponding to "not matchingAnd the successful fixed user i and the non-fixed user n are matched pairs successfully.
And 4, repeating the matching judgment.
In all successfully matched matching pairs, judging whether the same non-fixed user n forms a matching pair with a plurality of fixed users:
if yes, repeated matching exists, and step 5 is executed;
if not, then no repeated matching exists, then executing step 6;
and 5, breaking repeated matching.
Comparing the sizes of the utility functions of the repeatedly matched matching pairs, reserving the matching pair with the largest utility function, and breaking the rest matching pairs;
returning the broken matching pairs to the step 3, and selecting the suboptimal non-fixed users by the rest fixed users.
And 6, detecting the final condition of the matching completion.
Comparing the successful match logarithm to the total match logarithm;
if the values of the two are equal, the user matching is judged to be completed, and all matching pairs are subjected to non-orthogonal multiple access (NOMA) transmission according to the matching completing sequence to complete the data unloading of the Mobile Edge Computing (MEC);
otherwise, returning to the step 3. This can be further illustrated by the following simulations:
1. simulation conditions are as follows:
the millimeter wave network comprises 9 macro base stations and 27 micro base stations, and the interval between every two macro base stations is 1 kilometer. And setting a communication frequency band as a W frequency band, setting the available total bandwidth as 1GHz, setting the maximum transmission power of each macro base station as 46dBm and the noise power as-174 dBm/Hz.
2. Simulation content:
simulation 1, namely performing MEC task unloading transmission simulation on the millimeter wave network by respectively using the circular game algorithm and the existing greedy algorithm, and calculating an energy efficiency-delay function value when the matching logarithm is changed, wherein the result is shown in figure 2. The abscissa is the number of matching groups, and the ordinate is the energy efficiency-delay tradeoff value required by 10000 times of simulation averaging.
As can be seen from the simulation result of fig. 2, under the same number of matching groups, the energy efficiency-delay tradeoff value of the millimeter wave network for performing MEC task offloading transmission by using the method of the present invention is lower than the energy efficiency-delay tradeoff function value by using the greedy algorithm, and the advantages of the method of the present invention are more obvious as the number of matching groups increases.
And 2, performing MEC task unloading transmission simulation on the millimeter wave network by respectively using the method and the conventional greedy algorithm, and calculating the simulation of the energy efficiency-time delay balance value when the total transmission energy is changed, wherein the result is shown in FIG. 3. Wherein, the abscissa is total energy, and the ordinate is energy efficiency-time delay balance value required averagely in simulation 10000 times. As can be seen from the simulation result in fig. 3, under the condition of a certain total energy, the energy efficiency-delay tradeoff value of the millimeter wave network for performing MEC task offloading transmission by using the method of the present invention is lower than the energy efficiency-delay tradeoff value by using the greedy algorithm, and as the total energy increases, the energy efficiency-delay of the method of the present invention decreases more than the greedy algorithm.

Claims (5)

1. A millimeter wave MEC unloading transmission method based on a circular game algorithm is characterized by comprising the following steps:
1) the non-cooperative game theory method is utilized to set user matching parameters during unloading transmission:
setting transmission power, transmission rate, matching probability and penalty coefficient;
setting a fixed user and a non-fixed user;
setting a pairing set NP, an unpaired set UP and a paired set AP of the current round;
defining a reward function as the product of the matching probability and the transmission rate;
defining a penalty function as a linear weighting of the transmission power and its inverse;
2) each fixed user and each non-fixed user form a matching pair, and the reward function R of each matching pair is calculatedi,j(S) and a penalty function Ci,j(S) and summing the two functions to obtain a utility function Ui,j(S) wherein i represents the i-th fixingJ represents the jth non-fixed user, and S represents the pairing set type;
3) according to the maximum utility function rule, fixed users m sequentially search optimal non-fixed users n for matching;
4) judging whether the current matching pair in 3) and the existing matching pair have the same non-fixed user n:
if so, perform 5);
if not, then execute 6);
5) comparing the utility function of the current matching pair in 3) with the utility function of the 'existing matching pair' of the non-fixed user n:
if 3) utility function U of current matching pairm,n(S) is larger, the corresponding matching pair (m, n) is successfully matched, and the existing matching pair is broken;
otherwise, returning to 3), the fixed user m selects the non-fixed user n' with suboptimal performance;
6) checking whether all fixed users finish pairing:
if yes, completing user matching, and performing unloading transmission on each group of paired users based on the NOMA mechanism to complete MEC data unloading;
otherwise, return to 3).
2. The method of claim 1, wherein (2) a reward function R is calculated for each matching pairi,j(S) by the following formula:
Ri,j(S)＝βi,j(S)·r
where r represents the transmission rate,
Figure FDA0002916674370000021
for the matching probability function, N represents the total number of matched pairs, p represents the probability of selecting the current round of matched set NP, and q represents the probability of selecting the current round of unpaired set UP.
3. The method of claim 1, wherein (2) a penalty function C is computed for each matched pairi,j(S) by the following formula:
Ci,j(S)＝k1·W+k2/W，
wherein k is1,k2Are two penalty coefficients with different values, W is the transmission power.
4. The method of claim 1, wherein the utility function U obtained in (2)i,j(S), expressed as follows:
Figure FDA0002916674370000022
wherein r represents transmission rate, N represents total number of matched pairs, p represents probability of selecting matched set NP in the current round, q represents probability of selecting unpaired set UP in the current round, and k represents transmission rate1,k2Are two penalty coefficients with different values, W is the transmission power.
5. The method of claim 1, wherein in (3), the non-stationary users are sequentially matched with all the non-stationary users according to the maximum utility function rule, and the following is implemented:
5.1) forming a matching pair by a fixed user i and all non-fixed users j, and calculating utility function values U of all matching pairsi,j(S)，j∈{1,2,…N}；
5.2) comparison of Ui,1(S)～Ui,N(S) obtaining a utility function maximum value Ui,nAnd (S), the fixed user i and the non-fixed user n are matched pairs which are successfully matched.
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